We chose such a basic prediction model as decision trees are the textbook example for interpretable machine learning. It is worth noting that more accurate models can be obtained by using more advanced classification models. For example, using LightGBM [1]}, a highy efficient framework for Gradient Tree Boosting. However, such results can be highly dependent on hyperparameter tuning and it becomes challenging to guarantee a fair comparison between the different sampling approaches. Moreover, the models become much more difficult to interpret as in order to achieve good results one needs hundreds and sometimes thousands of trees for an optimal model.
